Network pharmacology and fingerprint for the integrated analysis of mechanism, identification and prediction in Panax notoginseng
文献类型： 外文期刊
作者： Liu, Chunlu 1 ; Xu, Furong 2 ; Zuo, Zhitian 1 ; Wang, Yuanzhong 1 ;
作者机构： 1.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming, Yunnan, Peoples R China
2.Yunnan Univ Chinese Med, Coll Tradit Chinese Med, Kunming, Yunnan, Peoples R China
3.Yunnan Acad Agr Sci, Med Plants Res Inst, 2238 Beijing Rd, Kunming 650200, Yunnan, Peoples R China
关键词： component content predict; fingerprint; network pharmacology; origin identification; Panax notoginseng; Q-marker action mechanism
期刊名称：PHYTOCHEMICAL ANALYSIS （ 影响因子：3.024； 五年影响因子：3.018 ）
ISSN： 0958-0344
年卷期：
页码：
收录情况： SCI
摘要： IntroductionPanax notoginseng (Burkill) F. H. Chen ex C. H. Chow, is a well-known herb with multitudinous efficacy. In this study, a series of overall analyses on the action mechanism, component content, origin identification, and content prediction of P. notoginseng are conducted. ObjectivesThe purpose was to analyse the mechanism of pharmacological efficacy, differences between contents and groups of P. notoginseng from different origins, and to identify the origin and predict the content. Materials and methodsThe P. notoginseng samples from four different origins were used for analysis by the database, network pharmacology (Q-marker) and fingerprint analysis [high-performance liquid chromatography (HPLC), attenuated total reflectance Fourier-transform infrared (ATR-FTIR) and near-infrared (NIR)] combined with data fusion strategy (low- and feature-level). ResultsFour saponins were identified as Q-markers, and exerted pharmacological effects on signalling pathways through 24 core targets. The qualitative and quantitative analysis of HPLC showed that there were differences among groups and different origins. Therefore, considering the need to treat diseases, combined with network database and network pharmacology, the suitable producing areas were determined through the mechanism of action and the required saponin content. The low-level data fusion successfully identified the origin and predicted the content of P. notoginseng from different origins. The accuracy rate of each evaluation index of the partial least squares discriminant analysis (PLS-DA) model was 1, and the t-SNE (t-distributed stochastic neighbor embedding) visualisation results were good. The coefficient of determination (R-2) of the partial least squares regression (PLSR) model ranged from 0.9235-0.9996, and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) range is 0.301-1.519. ConclusionThis study was designed to provide a sufficient theoretical basis for the quality control of P. notoginseng.
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